Computing and Information Systems - Theses
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ItemDiscovering syntactic phenomena with and within precision grammars( 2018)Precision grammars are hand-crafted computational models of human languages that are capable of parsing text to yield syntactic and semantic analyses. They are valuable for applications requiring the accurate extraction of semantic relationships and they also enable hypothesis testing of holistic grammatical theories over quantities of text impossible to analyse manually. Their capacity to generate linguistically accurate analyses over corpus data also supports another application: augmenting linguistic descriptions with query facilities for retrieving examples of syntactic phenomena. In order to construct such queries, it is first necessary to identify the signature of target syntactic phenomena within the analyses produced by the precision grammar in use. This is often a difficult process, however, as analyses within the descriptive grammar can diverge from those in the precision grammar due to differing theoretical assumptions made by the two resources, the use of different sets of data to inform their respective analyses, and the exigencies of implementing a large-scale formalised analyses. In this thesis, I present my research into developing methods for improving the discoverability of syntactic phenomena within precision grammars. This includes the construction of a corpus annotated with syntactic phenomena which supports the development of syntactic phenomenon discovery methodologies. Included within this context is the investigation of strategies for measuring inter-annotator agreement over textual annotations for which annotators both segment and label text---a property that traditional kappa-like measures do not support. The second facet of my research involves the development of an interactive methodology—and accompanying implementation—for navigating the alignment between dynamic characterisations of syntactic phenomena and the internal components of HPSG precision grammars associated with these phenomena. In addition to supporting the enhancement of descriptive grammars with precision grammars, this methodology has the potential to improve the accessibility of precision grammars themselves, enabling people not involved in their development to explore their internals using familiar syntactic phenomena, as well as allowing grammar engineers to navigate their grammars through the lens of analyses that are different to those found in the grammar.
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ItemThe effects of sampling and semantic categories on large-scale supervised relation extraction( 2012)The purpose of relation extraction is to identify novel pairs of entities which are related by a pre-specified relation such as hypernym or synonym. The traditional approach to relation extraction is to building a dedicated system for a particular relation, meaning that significant effort is required to repurpose the approach to new relations. We propose a generic approach based on supervised learning, which provides a standardised process for performing relation extraction on different relations and domains. We explore the feasibility of the approach over a range of relations and corpora, focusing particularly on the development of a realistic evaluation methodology for relation extraction. In addition to this, we investigate the impact of semantic categories on extraction effectiveness.